WLI-FCM and Artificial Neural Network Based Cloud Intrusion Detection System

Security and Performance aspects of cloud computing are the major issues which have to be tended to in Cloud Computing. Intrusion is one such basic and imperative security problem for Cloud Computing. Consequently, it is essential to create an Intrusion Detection System (IDS) to detect both inside and outside assaults with high detection precision in cloud environment. In this paper, cloud intrusion detection system at hypervisor layer is developed and assesses to detect the depraved activities in cloud computing environment. The cloud intrusion detection system uses a hybrid algorithm which is a fusion of WLI- FCM clustering algorithm and Back propagation artificial Neural Network to improve the detection accuracy of the cloud intrusion detection system. The proposed system is implemented and compared with K-means and classic FCM. The DARPA's KDD cup dataset 1999 is used for simulation. From the detailed performance analysis, it is clear that the proposed system is able to detect the anomalies with high detection accuracy and low false alarm rate.